Despite the widespread proliferation of neural networks, the mechanisms through which they operate so successfully are not well understood. In this talk, we will first explore empirical and theoretical investigations into neural network training and generalization and what they can tell us about why deep learning works. Then, we will examine a recent line of work on algorithm learning. While neural networks typically excel at pattern matching tasks, we consider whether neural networks can learn algorithms that scale to problem instances orders of magnitude larger than those seen during training.
Speaker(s): Micah Goldblum
Agenda:
– Invited talk from Micah Goldblum, postdoctoral research fellow at New York University working with (https://research.facebook.com/people/lecun-yann/) and (https://cims.nyu.edu/~andrewgw/).
– Q/A Session
Virtual: https://events.vtools.ieee.org/m/410297